A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images

Xiaoli Li; Xiaoli Li

doi:10.3934/mbe.2023948

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21432-21450. doi: 10.3934/mbe.2023948

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Research article Special Issues

A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images

Xiaoli Li ^,

College of Physical Education and Health Management, Henan Finance University, Zhengzhou 450046, Henan, China

Academic Editor: José M Jimenez

Received: 06 October 2023 Revised: 07 November 2023 Accepted: 19 November 2023 Published: 04 December 2023

The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.
- 3D reconstruction,
- KD-tree algorithm,
- random sample consensus,
- image processing
Citation: Xiaoli Li. A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21432-21450. doi: 10.3934/mbe.2023948

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Abstract

The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.

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